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1. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas

Author: Hughes, Odessica, Bentley, Amy R., Breeze, Charles E., Aguet, Francois, Xu, Xiaoguang, Nadkarni, Girish, Sun, Quan, Lin, Bridget M., Gilliland, Thomas, Meyer, Mariah C., Du, Jiawen, Raffield, Laura M., Kramer, Holly, Morton, Robert W., Gouveia, Mateus H., Atkinson, Elizabeth G., Valladares-Salgado, Adan, Wacher-Rodarte, Niels, Dueker, Nicole D., Guo, Xiuqing, Hai, Yang, Adeyemo, Adebowale, Best, Lyle G., Cai, Jianwen, Chen, Guanjie, Chong, Michael, Doumatey, Ayo, Eales, James, Goodarzi, Mark O., Ipp, Eli, Irvin, Marguerite Ryan, Jiang, Minzhi, Jones, Alana C., Kooperberg, Charles, Krieger, Jose E., Lange, Ethan M., Lanktree, Matthew B., Lash, James P., Lotufo, Paulo A., Loos, Ruth J.F., Ha My, Vy Thi, Peralta-Romero, Jesús, Qi, Lihong, Raffel, Leslie J., Rich, Stephen S., Rodriquez, Erik J., Tarazona-Santos, Eduardo, Taylor, Kent D., Umans, Jason G., Wen, Jia, Young, Bessie A., Yu, Zhi, Zhang, Ying, Ida Chen, Yii Der, Rundek, Tanja, Rotter, Jerome I., Cruz, Miguel, Fornage, Myriam, Lima-Costa, Maria Fernanda, Pereira, Alexandre C., Paré, Guillaume, Natarajan, Pradeep, Cole, Shelley A., Carson, April P., Lange, Leslie A., Li, Yun, Perez-Stable, Eliseo J., Do, Ron, Charchar, Fadi J., Tomaszewski, Maciej, Mychaleckyj, Josyf C., Rotimi, Charles, Morris, Andrew P., Franceschini, Nora, Hughes, Odessica, Bentley, Amy R., Breeze, Charles E., Aguet, Francois, Xu, Xiaoguang, Nadkarni, Girish, Sun, Quan, Lin, Bridget M., Gilliland, Thomas, Meyer, Mariah C., Du, Jiawen, Raffield, Laura M., Kramer, Holly, Morton, Robert W., Gouveia, Mateus H., Atkinson, Elizabeth G., Valladares-Salgado, Adan, Wacher-Rodarte, Niels, Dueker, Nicole D., Guo, Xiuqing, Hai, Yang, Adeyemo, Adebowale, Best, Lyle G., Cai, Jianwen, Chen, Guanjie, Chong, Michael, Doumatey, Ayo, Eales, James, Goodarzi, Mark O., Ipp, Eli, Irvin, Marguerite Ryan, Jiang, Minzhi, Jones, Alana C., Kooperberg, Charles, Krieger, Jose E., Lange, Ethan M., Lanktree, Matthew B., Lash, James P., Lotufo, Paulo A., Loos, Ruth J.F., Ha My, Vy Thi, Peralta-Romero, Jesús, Qi, Lihong, Raffel, Leslie J., Rich, Stephen S., Rodriquez, Erik J., Tarazona-Santos, Eduardo, Taylor, Kent D., Umans, Jason G., Wen, Jia, Young, Bessie A., Yu, Zhi, Zhang, Ying, Ida Chen, Yii Der, Rundek, Tanja, Rotter, Jerome I., Cruz, Miguel, Fornage, Myriam, Lima-Costa, Maria Fernanda, Pereira, Alexandre C., Paré, Guillaume, Natarajan, Pradeep, Cole, Shelley A., Carson, April P., Lange, Leslie A., Li, Yun, Perez-Stable, Eliseo J., Do, Ron, Charchar, Fadi J., Tomaszewski, Maciej, Mychaleckyj, Josyf C., Rotimi, Charles, Morris, Andrew P., and Franceschini, Nora
Abstract: Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10−8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.
Published: 2024

2. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas

Author: Hughes, Odessica, Bentley, Amy R., Breeze, Charles E., Aguet, Francois, Xu, Xiaoguang, Nadkarni, Girish, Sun, Quan, Lin, Bridget M., Gilliland, Thomas, Meyer, Mariah C., Du, Jiawen, Raffield, Laura M., Kramer, Holly, Morton, Robert W., Gouveia, Mateus H., Atkinson, Elizabeth G., Valladares-Salgado, Adan, Wacher-Rodarte, Niels, Dueker, Nicole D., Guo, Xiuqing, Hai, Yang, Adeyemo, Adebowale, Best, Lyle G., Cai, Jianwen, Chen, Guanjie, Chong, Michael, Doumatey, Ayo, Eales, James, Goodarzi, Mark O., Ipp, Eli, Irvin, Marguerite Ryan, Jiang, Minzhi, Jones, Alana C., Kooperberg, Charles, Krieger, Jose E., Lange, Ethan M., Lanktree, Matthew B., Lash, James P., Lotufo, Paulo A., Loos, Ruth J.F., Ha My, Vy Thi, Peralta-Romero, Jesús, Qi, Lihong, Raffel, Leslie J., Rich, Stephen S., Rodriquez, Erik J., Tarazona-Santos, Eduardo, Taylor, Kent D., Umans, Jason G., Wen, Jia, Young, Bessie A., Yu, Zhi, Zhang, Ying, Ida Chen, Yii Der, Rundek, Tanja, Rotter, Jerome I., Cruz, Miguel, Fornage, Myriam, Lima-Costa, Maria Fernanda, Pereira, Alexandre C., Paré, Guillaume, Natarajan, Pradeep, Cole, Shelley A., Carson, April P., Lange, Leslie A., Li, Yun, Perez-Stable, Eliseo J., Do, Ron, Charchar, Fadi J., Tomaszewski, Maciej, Mychaleckyj, Josyf C., Rotimi, Charles, Morris, Andrew P., Franceschini, Nora, Hughes, Odessica, Bentley, Amy R., Breeze, Charles E., Aguet, Francois, Xu, Xiaoguang, Nadkarni, Girish, Sun, Quan, Lin, Bridget M., Gilliland, Thomas, Meyer, Mariah C., Du, Jiawen, Raffield, Laura M., Kramer, Holly, Morton, Robert W., Gouveia, Mateus H., Atkinson, Elizabeth G., Valladares-Salgado, Adan, Wacher-Rodarte, Niels, Dueker, Nicole D., Guo, Xiuqing, Hai, Yang, Adeyemo, Adebowale, Best, Lyle G., Cai, Jianwen, Chen, Guanjie, Chong, Michael, Doumatey, Ayo, Eales, James, Goodarzi, Mark O., Ipp, Eli, Irvin, Marguerite Ryan, Jiang, Minzhi, Jones, Alana C., Kooperberg, Charles, Krieger, Jose E., Lange, Ethan M., Lanktree, Matthew B., Lash, James P., Lotufo, Paulo A., Loos, Ruth J.F., Ha My, Vy Thi, Peralta-Romero, Jesús, Qi, Lihong, Raffel, Leslie J., Rich, Stephen S., Rodriquez, Erik J., Tarazona-Santos, Eduardo, Taylor, Kent D., Umans, Jason G., Wen, Jia, Young, Bessie A., Yu, Zhi, Zhang, Ying, Ida Chen, Yii Der, Rundek, Tanja, Rotter, Jerome I., Cruz, Miguel, Fornage, Myriam, Lima-Costa, Maria Fernanda, Pereira, Alexandre C., Paré, Guillaume, Natarajan, Pradeep, Cole, Shelley A., Carson, April P., Lange, Leslie A., Li, Yun, Perez-Stable, Eliseo J., Do, Ron, Charchar, Fadi J., Tomaszewski, Maciej, Mychaleckyj, Josyf C., Rotimi, Charles, Morris, Andrew P., and Franceschini, Nora
Abstract: Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10−8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.
Published: 2024

3. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

Author: Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, Di Angelantonio, Emanuele, Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, and Di Angelantonio, Emanuele
Abstract: Background The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. Methods For this observational study, we conducted a combined analysis of individual-participant data from 19 high income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23 center dot 1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. Findings For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2 center dot 69 (95% CI 2 center dot 43-2 center dot 97) when diagnosed at 30-39 years, 2 center dot 26 (2 center dot 08-2 center dot 45) at 40-49 years, 1 center dot 84 (1 center dot 72-1 center dot 97) at 50-59 years, 1 center dot 57 (1 center dot 47-1 center dot 67) at 60-69 years, and 1 center dot 39 (1 center dot 29-1 center dot 51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier
Published: 2023
Full Text: View/download PDF

4. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

Author: Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, Di Angelantonio, Emanuele, Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, and Di Angelantonio, Emanuele
Abstract: Background The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. Methods For this observational study, we conducted a combined analysis of individual-participant data from 19 high income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23 center dot 1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. Findings For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2 center dot 69 (95% CI 2 center dot 43-2 center dot 97) when diagnosed at 30-39 years, 2 center dot 26 (2 center dot 08-2 center dot 45) at 40-49 years, 1 center dot 84 (1 center dot 72-1 center dot 97) at 50-59 years, 1 center dot 57 (1 center dot 47-1 center dot 67) at 60-69 years, and 1 center dot 39 (1 center dot 29-1 center dot 51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier
Published: 2023
Full Text: View/download PDF

5. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

Author: Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, Di Angelantonio, Emanuele, Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, and Di Angelantonio, Emanuele
Abstract: Background The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. Methods For this observational study, we conducted a combined analysis of individual-participant data from 19 high income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23 center dot 1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. Findings For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2 center dot 69 (95% CI 2 center dot 43-2 center dot 97) when diagnosed at 30-39 years, 2 center dot 26 (2 center dot 08-2 center dot 45) at 40-49 years, 1 center dot 84 (1 center dot 72-1 center dot 97) at 50-59 years, 1 center dot 57 (1 center dot 47-1 center dot 67) at 60-69 years, and 1 center dot 39 (1 center dot 29-1 center dot 51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier
Published: 2023
Full Text: View/download PDF

6. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

Author: Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, Di Angelantonio, Emanuele, Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, and Di Angelantonio, Emanuele
Abstract: Background The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. Methods For this observational study, we conducted a combined analysis of individual-participant data from 19 high income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23 center dot 1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. Findings For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2 center dot 69 (95% CI 2 center dot 43-2 center dot 97) when diagnosed at 30-39 years, 2 center dot 26 (2 center dot 08-2 center dot 45) at 40-49 years, 1 center dot 84 (1 center dot 72-1 center dot 97) at 50-59 years, 1 center dot 57 (1 center dot 47-1 center dot 67) at 60-69 years, and 1 center dot 39 (1 center dot 29-1 center dot 51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier
Published: 2023
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7. Lipidomics profiling of biological aging in American Indians: the Strong Heart Family Study.

Author: Subedi, Pooja, Subedi, Pooja, Palma-Gudiel, Helena, Fiehn, Oliver, Best, Lyle G, Lee, Elisa T, Howard, Barbara V, Zhao, Jinying, Subedi, Pooja, Subedi, Pooja, Palma-Gudiel, Helena, Fiehn, Oliver, Best, Lyle G, Lee, Elisa T, Howard, Barbara V, and Zhao, Jinying
Abstract: Telomeres shorten with age and shorter leukocyte telomere length (LTL) has been associated with various age-related diseases. Thus, LTL has been considered a biomarker of biological aging. Dyslipidemia is an established risk factor for most age-related metabolic disorders. However, little is known about the relationship between LTL and dyslipidemia. Lipidomics is a new biochemical technique that can simultaneously identify and quantify hundreds to thousands of small molecular lipid species. In a large population comprising 1843 well-characterized American Indians in the Strong Heart Family Study, we examined the lipidomic profile of biological aging assessed by LTL. Briefly, LTL was quantified by qPCR. Fasting plasma lipids were quantified by untargeted liquid chromatography-mass spectrometry. Lipids associated with LTL were identified by elastic net modeling. Of 1542 molecular lipids identified (518 known, 1024 unknown), 174 lipids (36 knowns) were significantly associated with LTL, independent of chronological age, sex, BMI, hypertension, diabetes status, smoking status, bulk HDL-C, and LDL-C. These findings suggest that altered lipid metabolism is associated with biological aging and provide novel insights that may enhance our understanding of the relationship between dyslipidemia, biological aging, and age-related diseases in American Indians.
Published: 2023

8. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

Author: Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, Di Angelantonio, Emanuele, Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, and Di Angelantonio, Emanuele
Abstract: Background The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. Methods For this observational study, we conducted a combined analysis of individual-participant data from 19 high income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23 center dot 1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. Findings For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2 center dot 69 (95% CI 2 center dot 43-2 center dot 97) when diagnosed at 30-39 years, 2 center dot 26 (2 center dot 08-2 center dot 45) at 40-49 years, 1 center dot 84 (1 center dot 72-1 center dot 97) at 50-59 years, 1 center dot 57 (1 center dot 47-1 center dot 67) at 60-69 years, and 1 center dot 39 (1 center dot 29-1 center dot 51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier
Published: 2023
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9. Diversifying the genomic data science research community

Author: Network, The Genomic Data Science Community, Network, The Genomic Data Science Community, Alcazar, Rosa, Alvarez, Maria, Arnold, Rachel, Ayalew, Mentewab, Best, Lyle G, Campbell, Michael C, Chowdhury, Kamal, Cox, Katherine EL, Daulton, Christina, Deng, Youping, Easter, Carla, Fuller, Karla, Hakim, Shazia Tabassum, Hoffman, Ava M, Kucher, Natalie, Lee, Andrew, Lee, Joslynn, Leek, Jeffrey T, Meller, Robert, Méndez, Loyda B, Méndez-González, Miguel P, Mosher, Stephen, Nishiguchi, Michele, Pratap, Siddharth, Rolle, Tiffany, Roy, Sourav, Saidi, Rachel, Schatz, Michael C, Sen, Shurjo K, Sniezek, James, Martinez, Edu Suarez, Tan, Frederick J, Vessio, Jennifer, Watson, Karriem, Westbroek, Wendy, Wilcox, Joseph, Wright, Carrie, Xie, Xianfa, Network, The Genomic Data Science Community, Network, The Genomic Data Science Community, Alcazar, Rosa, Alvarez, Maria, Arnold, Rachel, Ayalew, Mentewab, Best, Lyle G, Campbell, Michael C, Chowdhury, Kamal, Cox, Katherine EL, Daulton, Christina, Deng, Youping, Easter, Carla, Fuller, Karla, Hakim, Shazia Tabassum, Hoffman, Ava M, Kucher, Natalie, Lee, Andrew, Lee, Joslynn, Leek, Jeffrey T, Meller, Robert, Méndez, Loyda B, Méndez-González, Miguel P, Mosher, Stephen, Nishiguchi, Michele, Pratap, Siddharth, Rolle, Tiffany, Roy, Sourav, Saidi, Rachel, Schatz, Michael C, Sen, Shurjo K, Sniezek, James, Martinez, Edu Suarez, Tan, Frederick J, Vessio, Jennifer, Watson, Karriem, Westbroek, Wendy, Wilcox, Joseph, Wright, Carrie, and Xie, Xianfa
Abstract: Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support.
Published: 2022

10. Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease.

Author: Domingo-Relloso, Arce, Domingo-Relloso, Arce, Makhani, Kiran, Riffo-Campos, Angela L, Tellez-Plaza, Maria, Klein, Kathleen Oros, Subedi, Pooja, Zhao, Jinying, Moon, Katherine A, Bozack, Anne K, Haack, Karin, Goessler, Walter, Umans, Jason G, Best, Lyle G, Zhang, Ying, Herreros-Martinez, Miguel, Glabonjat, Ronald A, Schilling, Kathrin, Galvez-Fernandez, Marta, Kent, Jack W, Sanchez, Tiffany R, Taylor, Kent D, Johnson, W Craig, Durda, Peter, Tracy, Russell P, Rotter, Jerome I, Rich, Stephen S, Van Den Berg, David, Kasela, Silva, Lappalainen, Tuuli, Vasan, Ramachandran S, Joehanes, Roby, Howard, Barbara V, Levy, Daniel, Lohman, Kurt, Liu, Yongmei, Fallin, M Daniele, Cole, Shelley A, Mann, Koren K, Navas-Acien, Ana, Domingo-Relloso, Arce, Domingo-Relloso, Arce, Makhani, Kiran, Riffo-Campos, Angela L, Tellez-Plaza, Maria, Klein, Kathleen Oros, Subedi, Pooja, Zhao, Jinying, Moon, Katherine A, Bozack, Anne K, Haack, Karin, Goessler, Walter, Umans, Jason G, Best, Lyle G, Zhang, Ying, Herreros-Martinez, Miguel, Glabonjat, Ronald A, Schilling, Kathrin, Galvez-Fernandez, Marta, Kent, Jack W, Sanchez, Tiffany R, Taylor, Kent D, Johnson, W Craig, Durda, Peter, Tracy, Russell P, Rotter, Jerome I, Rich, Stephen S, Van Den Berg, David, Kasela, Silva, Lappalainen, Tuuli, Vasan, Ramachandran S, Joehanes, Roby, Howard, Barbara V, Levy, Daniel, Lohman, Kurt, Liu, Yongmei, Fallin, M Daniele, Cole, Shelley A, Mann, Koren K, and Navas-Acien, Ana
Abstract: BackgroundEpigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD.MethodsBlood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE-/-) mouse model of atherosclerosis.ResultsA total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic.ConclusionsDifferential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.
Published: 2022

11. Diversifying the Genomic Data Science Research Community

Author: Network, The Genomic Data Science Community, Alcazar, Rosa, Alvarez, Maria, Arnold, Rachel, Ayalew, Mentewab, Best, Lyle G., Campbell, Michael C., Chowdhury, Kamal, Cox, Katherine E. L., Daulton, Christina, Deng, Youping, Easter, Carla, Fuller, Karla, Hakim, Shazia Tabassum, Hoffman, Ava M., Kucher, Natalie, Lee, Andrew, Lee, Joslynn, Leek, Jeffrey T., Meller, Robert, Méndez, Loyda B., Méndez-González, Miguel P., Mosher, Stephen, Nishiguchi, Michele, Pratap, Siddharth, Rolle, Tiffany, Roy, Sourav, Saidi, Rachel, Schatz, Michael C., Sen, Shurjo, Sniezek, James, Martinez, Edu Suarez, Tan, Frederick, Vessio, Jennifer, Watson, Karriem, Westbroek, Wendy, Wilcox, Joseph, Xie, Xianfa, Network, The Genomic Data Science Community, Alcazar, Rosa, Alvarez, Maria, Arnold, Rachel, Ayalew, Mentewab, Best, Lyle G., Campbell, Michael C., Chowdhury, Kamal, Cox, Katherine E. L., Daulton, Christina, Deng, Youping, Easter, Carla, Fuller, Karla, Hakim, Shazia Tabassum, Hoffman, Ava M., Kucher, Natalie, Lee, Andrew, Lee, Joslynn, Leek, Jeffrey T., Meller, Robert, Méndez, Loyda B., Méndez-González, Miguel P., Mosher, Stephen, Nishiguchi, Michele, Pratap, Siddharth, Rolle, Tiffany, Roy, Sourav, Saidi, Rachel, Schatz, Michael C., Sen, Shurjo, Sniezek, James, Martinez, Edu Suarez, Tan, Frederick, Vessio, Jennifer, Watson, Karriem, Westbroek, Wendy, Wilcox, Joseph, and Xie, Xianfa
Abstract: Over the last 20 years, there has been an explosion of genomic data collected for disease association, functional analyses, and other large-scale discoveries. At the same time, there have been revolutions in cloud computing that enable computational and data science research, while making data accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support students, faculty, and administrators at Underserved Institutions (UIs) including Community Colleges, Historically Black Colleges and Universities, Hispanic-Serving Institutions, and Tribal Colleges and Universities in taking advantage of these tools in local educational and research programs. We have formed the Genomic Data Science Community Network (http://www.gdscn.org/) to identify opportunities and support broadening access to cloud-enabled genomic data science. Here, we provide a summary of the priorities for faculty members at UIs, as well as administrators, funders, and R1 researchers to consider as we create a more diverse genomic data science community., Comment: 42 pages, 3 figures
Published: 2022

12. Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study.

Author: Kocarnik, Jonathan M, Kocarnik, Jonathan M, Richard, Melissa, Graff, Misa, Haessler, Jeffrey, Bien, Stephanie, Carlson, Chris, Carty, Cara L, Reiner, Alexander P, Avery, Christy L, Ballantyne, Christie M, LaCroix, Andrea Z, Assimes, Themistocles L, Barbalic, Maja, Pankratz, Nathan, Tang, Weihong, Tao, Ran, Chen, Dongquan, Talavera, Gregory A, Daviglus, Martha L, Chirinos-Medina, Diana A, Pereira, Rocio, Nishimura, Katie, Bužková, Petra, Best, Lyle G, Ambite, José Luis, Cheng, Iona, Crawford, Dana C, Hindorff, Lucia A, Fornage, Myriam, Heiss, Gerardo, North, Kari E, Haiman, Christopher A, Peters, Ulrike, Le Marchand, Loic, Kooperberg, Charles, Kocarnik, Jonathan M, Kocarnik, Jonathan M, Richard, Melissa, Graff, Misa, Haessler, Jeffrey, Bien, Stephanie, Carlson, Chris, Carty, Cara L, Reiner, Alexander P, Avery, Christy L, Ballantyne, Christie M, LaCroix, Andrea Z, Assimes, Themistocles L, Barbalic, Maja, Pankratz, Nathan, Tang, Weihong, Tao, Ran, Chen, Dongquan, Talavera, Gregory A, Daviglus, Martha L, Chirinos-Medina, Diana A, Pereira, Rocio, Nishimura, Katie, Bužková, Petra, Best, Lyle G, Ambite, José Luis, Cheng, Iona, Crawford, Dana C, Hindorff, Lucia A, Fornage, Myriam, Heiss, Gerardo, North, Kari E, Haiman, Christopher A, Peters, Ulrike, Le Marchand, Loic, and Kooperberg, Charles
Abstract: C-reactive protein (CRP) is a circulating biomarker indicative of systemic inflammation. We aimed to evaluate genetic associations with CRP levels among non-European-ancestry populations through discovery, fine-mapping and conditional analyses. A total of 30 503 non-European-ancestry participants from 6 studies participating in the Population Architecture using Genomics and Epidemiology study had serum high-sensitivity CRP measurements and ∼200 000 single nucleotide polymorphisms (SNPs) genotyped on the Metabochip. We evaluated the association between each SNP and log-transformed CRP levels using multivariate linear regression, with additive genetic models adjusted for age, sex, the first four principal components of genetic ancestry, and study-specific factors. Differential linkage disequilibrium patterns between race/ethnicity groups were used to fine-map regions associated with CRP levels. Conditional analyses evaluated for multiple independent signals within genetic regions. One hundred and sixty-three unique variants in 12 loci in overall or race/ethnicity-stratified Metabochip-wide scans reached a Bonferroni-corrected P-value <2.5E-7. Three loci have no (HACL1, OLFML2B) or only limited (PLA2G6) previous associations with CRP levels. Six loci had different top hits in race/ethnicity-specific versus overall analyses. Fine-mapping refined the signal in six loci, particularly in HNF1A. Conditional analyses provided evidence for secondary signals in LEPR, IL1RN and HNF1A, and for multiple independent signals in CRP and APOE. We identified novel variants and loci associated with CRP levels, generalized known CRP associations to a multiethnic study population, refined association signals at several loci and found evidence for multiple independent signals at several well-known loci. This study demonstrates the benefit of conducting inclusive genetic association studies in large multiethnic populations.
Published: 2018

13. Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study.

Author: Kocarnik, Jonathan M, Kocarnik, Jonathan M, Richard, Melissa, Graff, Misa, Haessler, Jeffrey, Bien, Stephanie, Carlson, Chris, Carty, Cara L, Reiner, Alexander P, Avery, Christy L, Ballantyne, Christie M, LaCroix, Andrea Z, Assimes, Themistocles L, Barbalic, Maja, Pankratz, Nathan, Tang, Weihong, Tao, Ran, Chen, Dongquan, Talavera, Gregory A, Daviglus, Martha L, Chirinos-Medina, Diana A, Pereira, Rocio, Nishimura, Katie, Bužková, Petra, Best, Lyle G, Ambite, José Luis, Cheng, Iona, Crawford, Dana C, Hindorff, Lucia A, Fornage, Myriam, Heiss, Gerardo, North, Kari E, Haiman, Christopher A, Peters, Ulrike, Le Marchand, Loic, Kooperberg, Charles, Kocarnik, Jonathan M, Kocarnik, Jonathan M, Richard, Melissa, Graff, Misa, Haessler, Jeffrey, Bien, Stephanie, Carlson, Chris, Carty, Cara L, Reiner, Alexander P, Avery, Christy L, Ballantyne, Christie M, LaCroix, Andrea Z, Assimes, Themistocles L, Barbalic, Maja, Pankratz, Nathan, Tang, Weihong, Tao, Ran, Chen, Dongquan, Talavera, Gregory A, Daviglus, Martha L, Chirinos-Medina, Diana A, Pereira, Rocio, Nishimura, Katie, Bužková, Petra, Best, Lyle G, Ambite, José Luis, Cheng, Iona, Crawford, Dana C, Hindorff, Lucia A, Fornage, Myriam, Heiss, Gerardo, North, Kari E, Haiman, Christopher A, Peters, Ulrike, Le Marchand, Loic, and Kooperberg, Charles
Abstract: C-reactive protein (CRP) is a circulating biomarker indicative of systemic inflammation. We aimed to evaluate genetic associations with CRP levels among non-European-ancestry populations through discovery, fine-mapping and conditional analyses. A total of 30 503 non-European-ancestry participants from 6 studies participating in the Population Architecture using Genomics and Epidemiology study had serum high-sensitivity CRP measurements and ∼200 000 single nucleotide polymorphisms (SNPs) genotyped on the Metabochip. We evaluated the association between each SNP and log-transformed CRP levels using multivariate linear regression, with additive genetic models adjusted for age, sex, the first four principal components of genetic ancestry, and study-specific factors. Differential linkage disequilibrium patterns between race/ethnicity groups were used to fine-map regions associated with CRP levels. Conditional analyses evaluated for multiple independent signals within genetic regions. One hundred and sixty-three unique variants in 12 loci in overall or race/ethnicity-stratified Metabochip-wide scans reached a Bonferroni-corrected P-value <2.5E-7. Three loci have no (HACL1, OLFML2B) or only limited (PLA2G6) previous associations with CRP levels. Six loci had different top hits in race/ethnicity-specific versus overall analyses. Fine-mapping refined the signal in six loci, particularly in HNF1A. Conditional analyses provided evidence for secondary signals in LEPR, IL1RN and HNF1A, and for multiple independent signals in CRP and APOE. We identified novel variants and loci associated with CRP levels, generalized known CRP associations to a multiethnic study population, refined association signals at several loci and found evidence for multiple independent signals at several well-known loci. This study demonstrates the benefit of conducting inclusive genetic association studies in large multiethnic populations.
Published: 2018

14. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

15. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

16. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

17. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

18. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

19. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

20. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

21. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

22. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
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23. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction:An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017

24. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W.M.M., Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan L.J., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan Håkan, Willeit, Johann, Onat, Altan, De La Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew W., Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela M., Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W.M.M., Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan L.J., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan Håkan, Willeit, Johann, Onat, Altan, De La Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew W., Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela M.
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (Cindex) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017

25. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G., Psaty, Bruce M., Goldbourt, Uri, Best, Lyle G., Assmann, Gerd, Salonen, Jukka T., Nietert, Paul J., Verschuren, W. M. Monique, Brunner, Eric J., Kronmal, Richard A., Salomaa, Veikko, Bakker, Stephan J. L., Dagenais, Gilles R., Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W., Meade, Tom W., Donfrancesco, Chiara, Kuller, Lewis H., Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R., Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G., and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017
Full Text: View/download PDF

26. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction:An Individual-Participant-Data Meta-Analysis

Author: Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela
Abstract: The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Published: 2017

27. Arsenic Exposure, Arsenic Metabolism, and Incident Diabetes in the Strong Heart Study

Author: Kuo, Chin-Chi, Kuo, Chin-Chi, Howard, Barbara V, Umans, Jason G, Gribble, Matthew O, Best, Lyle G, Francesconi, Kevin A, Goessler, Walter, Lee, Elisa, Guallar, Eliseo, Navas-Acien, Ana, Kuo, Chin-Chi, Kuo, Chin-Chi, Howard, Barbara V, Umans, Jason G, Gribble, Matthew O, Best, Lyle G, Francesconi, Kevin A, Goessler, Walter, Lee, Elisa, Guallar, Eliseo, and Navas-Acien, Ana
Abstract: ObjectiveLittle is known about arsenic metabolism in diabetes development. We investigated the prospective associations of low-moderate arsenic exposure and arsenic metabolism with diabetes incidence in the Strong Heart Study.Research design and methodsA total of 1,694 diabetes-free participants aged 45-75 years were recruited in 1989-1991 and followed through 1998-1999. We used the proportions of urine inorganic arsenic (iAs), monomethylarsonate (MMA), and dimethylarsinate (DMA) over their sum (expressed as iAs%, MMA%, and DMA%) as the biomarkers of arsenic metabolism. Diabetes was defined as fasting glucose ≥ 126 mg/dL, 2-h glucose ≥ 200 mg/dL, self-reported diabetes history, or self-reported use of antidiabetic medications.ResultsOver 11,263.2 person-years of follow-up, 396 participants developed diabetes. Using the leave-one-out approach to model the dynamics of arsenic metabolism, we found that lower MMA% was associated with higher diabetes incidence. The hazard ratios (95% CI) of diabetes incidence for a 5% increase in MMA% were 0.77 (0.63-0.93) and 0.82 (0.73-0.92) when iAs% and DMA%, respectively, were left out of the model. DMA% was associated with higher diabetes incidence only when MMA% decreased (left out of the model) but not when iAs% decreased. iAs% was also associated with higher diabetes incidence when MMA% decreased. The association between MMA% and diabetes incidence was similar by age, sex, study site, obesity, and urine iAs concentrations.ConclusionsArsenic metabolism, particularly lower MMA%, was prospectively associated with increased incidence of diabetes. Research is needed to evaluate whether arsenic metabolism is related to diabetes incidence per se or through its close connections with one-carbon metabolism.
Published: 2015

28. Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

Author: Iyengar, Sudha K, Iyengar, Sudha K, Sedor, John R, Freedman, Barry I, Kao, WH Linda, Kretzler, Matthias, Keller, Benjamin J, Abboud, Hanna E, Adler, Sharon G, Best, Lyle G, Bowden, Donald W, Burlock, Allison, Chen, Yii-Der Ida, Cole, Shelley A, Comeau, Mary E, Curtis, Jeffrey M, Divers, Jasmin, Drechsler, Christiane, Duggirala, Ravi, Elston, Robert C, Guo, Xiuqing, Huang, Huateng, Hoffmann, Michael Marcus, Howard, Barbara V, Ipp, Eli, Kimmel, Paul L, Klag, Michael J, Knowler, William C, Kohn, Orly F, Leak, Tennille S, Leehey, David J, Li, Man, Malhotra, Alka, März, Winfried, Nair, Viji, Nelson, Robert G, Nicholas, Susanne B, O'Brien, Stephen J, Pahl, Madeleine V, Parekh, Rulan S, Pezzolesi, Marcus G, Rasooly, Rebekah S, Rotimi, Charles N, Rotter, Jerome I, Schelling, Jeffrey R, Seldin, Michael F, Shah, Vallabh O, Smiles, Adam M, Smith, Michael W, Taylor, Kent D, Thameem, Farook, Thornley-Brown, Denyse P, Truitt, Barbara J, Wanner, Christoph, Weil, E Jennifer, Winkler, Cheryl A, Zager, Philip G, Igo, Robert P, Hanson, Robert L, Langefeld, Carl D, Family Investigation of Nephropathy and Diabetes (FIND), Iyengar, Sudha K, Iyengar, Sudha K, Sedor, John R, Freedman, Barry I, Kao, WH Linda, Kretzler, Matthias, Keller, Benjamin J, Abboud, Hanna E, Adler, Sharon G, Best, Lyle G, Bowden, Donald W, Burlock, Allison, Chen, Yii-Der Ida, Cole, Shelley A, Comeau, Mary E, Curtis, Jeffrey M, Divers, Jasmin, Drechsler, Christiane, Duggirala, Ravi, Elston, Robert C, Guo, Xiuqing, Huang, Huateng, Hoffmann, Michael Marcus, Howard, Barbara V, Ipp, Eli, Kimmel, Paul L, Klag, Michael J, Knowler, William C, Kohn, Orly F, Leak, Tennille S, Leehey, David J, Li, Man, Malhotra, Alka, März, Winfried, Nair, Viji, Nelson, Robert G, Nicholas, Susanne B, O'Brien, Stephen J, Pahl, Madeleine V, Parekh, Rulan S, Pezzolesi, Marcus G, Rasooly, Rebekah S, Rotimi, Charles N, Rotter, Jerome I, Schelling, Jeffrey R, Seldin, Michael F, Shah, Vallabh O, Smiles, Adam M, Smith, Michael W, Taylor, Kent D, Thameem, Farook, Thornley-Brown, Denyse P, Truitt, Barbara J, Wanner, Christoph, Weil, E Jennifer, Winkler, Cheryl A, Zager, Philip G, Igo, Robert P, Hanson, Robert L, Langefeld, Carl D, and Family Investigation of Nephropathy and Diabetes (FIND)
Abstract: Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.
Published: 2015

29. Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

Author: Iyengar, Sudha K, Williams, Scott M1, Iyengar, Sudha K, Sedor, John R, Freedman, Barry I, Kao, WH Linda, Kretzler, Matthias, Keller, Benjamin J, Abboud, Hanna E, Adler, Sharon G, Best, Lyle G, Bowden, Donald W, Burlock, Allison, Chen, Yii-Der Ida, Cole, Shelley A, Comeau, Mary E, Curtis, Jeffrey M, Divers, Jasmin, Drechsler, Christiane, Duggirala, Ravi, Elston, Robert C, Guo, Xiuqing, Huang, Huateng, Hoffmann, Michael Marcus, Howard, Barbara V, Ipp, Eli, Kimmel, Paul L, Klag, Michael J, Knowler, William C, Kohn, Orly F, Leak, Tennille S, Leehey, David J, Li, Man, Malhotra, Alka, März, Winfried, Nair, Viji, Nelson, Robert G, Nicholas, Susanne B, O'Brien, Stephen J, Pahl, Madeleine V, Parekh, Rulan S, Pezzolesi, Marcus G, Rasooly, Rebekah S, Rotimi, Charles N, Rotter, Jerome I, Schelling, Jeffrey R, Seldin, Michael F, Shah, Vallabh O, Smiles, Adam M, Smith, Michael W, Taylor, Kent D, Thameem, Farook, Thornley-Brown, Denyse P, Truitt, Barbara J, Wanner, Christoph, Weil, E Jennifer, Winkler, Cheryl A, Zager, Philip G, Igo, Robert P, Hanson, Robert L, Langefeld, Carl D, Family Investigation of Nephropathy and Diabetes (FIND), Iyengar, Sudha K, Williams, Scott M1, Iyengar, Sudha K, Sedor, John R, Freedman, Barry I, Kao, WH Linda, Kretzler, Matthias, Keller, Benjamin J, Abboud, Hanna E, Adler, Sharon G, Best, Lyle G, Bowden, Donald W, Burlock, Allison, Chen, Yii-Der Ida, Cole, Shelley A, Comeau, Mary E, Curtis, Jeffrey M, Divers, Jasmin, Drechsler, Christiane, Duggirala, Ravi, Elston, Robert C, Guo, Xiuqing, Huang, Huateng, Hoffmann, Michael Marcus, Howard, Barbara V, Ipp, Eli, Kimmel, Paul L, Klag, Michael J, Knowler, William C, Kohn, Orly F, Leak, Tennille S, Leehey, David J, Li, Man, Malhotra, Alka, März, Winfried, Nair, Viji, Nelson, Robert G, Nicholas, Susanne B, O'Brien, Stephen J, Pahl, Madeleine V, Parekh, Rulan S, Pezzolesi, Marcus G, Rasooly, Rebekah S, Rotimi, Charles N, Rotter, Jerome I, Schelling, Jeffrey R, Seldin, Michael F, Shah, Vallabh O, Smiles, Adam M, Smith, Michael W, Taylor, Kent D, Thameem, Farook, Thornley-Brown, Denyse P, Truitt, Barbara J, Wanner, Christoph, Weil, E Jennifer, Winkler, Cheryl A, Zager, Philip G, Igo, Robert P, Hanson, Robert L, Langefeld, Carl D, and Family Investigation of Nephropathy and Diabetes (FIND)
Abstract: Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.
Published: 2015

30. Multiancestral analysis of inflammation-related genetic variants and C-reactive protein in the population architecture using genomics and epidemiology study.

Author: Kocarnik, Jonathan M, Kocarnik, Jonathan M, Pendergrass, Sarah A, Carty, Cara L, Pankow, James S, Schumacher, Fredrick R, Cheng, Iona, Durda, Peter, Ambite, José Luis, Deelman, Ewa, Cook, Nancy R, Liu, Simin, Wactawski-Wende, Jean, Hutter, Carolyn, Brown-Gentry, Kristin, Wilson, Sarah, Best, Lyle G, Pankratz, Nathan, Hong, Ching-Ping, Cole, Shelley A, Voruganti, V Saroja, Bůžkova, Petra, Jorgensen, Neal W, Jenny, Nancy S, Wilkens, Lynne R, Haiman, Christopher A, Kolonel, Laurence N, Lacroix, Andrea, North, Kari, Jackson, Rebecca, Le Marchand, Loic, Hindorff, Lucia A, Crawford, Dana C, Gross, Myron, Peters, Ulrike, Kocarnik, Jonathan M, Kocarnik, Jonathan M, Pendergrass, Sarah A, Carty, Cara L, Pankow, James S, Schumacher, Fredrick R, Cheng, Iona, Durda, Peter, Ambite, José Luis, Deelman, Ewa, Cook, Nancy R, Liu, Simin, Wactawski-Wende, Jean, Hutter, Carolyn, Brown-Gentry, Kristin, Wilson, Sarah, Best, Lyle G, Pankratz, Nathan, Hong, Ching-Ping, Cole, Shelley A, Voruganti, V Saroja, Bůžkova, Petra, Jorgensen, Neal W, Jenny, Nancy S, Wilkens, Lynne R, Haiman, Christopher A, Kolonel, Laurence N, Lacroix, Andrea, North, Kari, Jackson, Rebecca, Le Marchand, Loic, Hindorff, Lucia A, Crawford, Dana C, Gross, Myron, and Peters, Ulrike
Abstract: BackgroundC-reactive protein (CRP) is a biomarker of inflammation. Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with CRP concentrations and inflammation-related traits such as cardiovascular disease, type 2 diabetes mellitus, and obesity. We aimed to replicate previous CRP-SNP associations, assess whether these associations generalize to additional race/ethnicity groups, and evaluate inflammation-related SNPs for a potentially pleiotropic association with CRP.Methods and resultsWe selected and analyzed 16 CRP-associated and 250 inflammation-related GWAS SNPs among 40 473 African American, American Indian, Asian/Pacific Islander, European American, and Hispanic participants from 7 studies collaborating in the Population Architecture using Genomics and Epidemiology (PAGE) study. Fixed-effect meta-analyses combined study-specific race/ethnicity-stratified linear regression estimates to evaluate the association between each SNP and high-sensitivity CRP. Overall, 18 SNPs in 8 loci were significantly associated with CRP (Bonferroni-corrected P<3.1×10(-3) for replication, P<2.0×10(-4) for pleiotropy): Seven of these were specific to European Americans, while 9 additionally generalized to African Americans (1), Hispanics (5), or both (3); 1 SNP was seen only in African Americans and Hispanics. Two SNPs in the CELSR2/PSRC1/SORT1 locus showed a potentially novel association with CRP: rs599839 (P=2.0×10(-6)) and rs646776 (P=3.1×10(-5)).ConclusionsWe replicated 16 SNP-CRP associations, 10 of which generalized to African Americans and/or Hispanics. We also identified potentially novel pleiotropic associations with CRP for two SNPs previously associated with coronary artery disease and/or low-density lipoprotein-cholesterol. These findings demonstrate the benefit of evaluating genotype-phenotype associations in multiple race/ethnicity groups and looking for pleiotropic relationships among SNPs previously associated
Published: 2014

31. Multiancestral analysis of inflammation-related genetic variants and C-reactive protein in the population architecture using genomics and epidemiology study.

Author: Kocarnik, Jonathan M, Kocarnik, Jonathan M, Pendergrass, Sarah A, Carty, Cara L, Pankow, James S, Schumacher, Fredrick R, Cheng, Iona, Durda, Peter, Ambite, José Luis, Deelman, Ewa, Cook, Nancy R, Liu, Simin, Wactawski-Wende, Jean, Hutter, Carolyn, Brown-Gentry, Kristin, Wilson, Sarah, Best, Lyle G, Pankratz, Nathan, Hong, Ching-Ping, Cole, Shelley A, Voruganti, V Saroja, Bůžkova, Petra, Jorgensen, Neal W, Jenny, Nancy S, Wilkens, Lynne R, Haiman, Christopher A, Kolonel, Laurence N, Lacroix, Andrea, North, Kari, Jackson, Rebecca, Le Marchand, Loic, Hindorff, Lucia A, Crawford, Dana C, Gross, Myron, Peters, Ulrike, Kocarnik, Jonathan M, Kocarnik, Jonathan M, Pendergrass, Sarah A, Carty, Cara L, Pankow, James S, Schumacher, Fredrick R, Cheng, Iona, Durda, Peter, Ambite, José Luis, Deelman, Ewa, Cook, Nancy R, Liu, Simin, Wactawski-Wende, Jean, Hutter, Carolyn, Brown-Gentry, Kristin, Wilson, Sarah, Best, Lyle G, Pankratz, Nathan, Hong, Ching-Ping, Cole, Shelley A, Voruganti, V Saroja, Bůžkova, Petra, Jorgensen, Neal W, Jenny, Nancy S, Wilkens, Lynne R, Haiman, Christopher A, Kolonel, Laurence N, Lacroix, Andrea, North, Kari, Jackson, Rebecca, Le Marchand, Loic, Hindorff, Lucia A, Crawford, Dana C, Gross, Myron, and Peters, Ulrike
Abstract: BackgroundC-reactive protein (CRP) is a biomarker of inflammation. Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with CRP concentrations and inflammation-related traits such as cardiovascular disease, type 2 diabetes mellitus, and obesity. We aimed to replicate previous CRP-SNP associations, assess whether these associations generalize to additional race/ethnicity groups, and evaluate inflammation-related SNPs for a potentially pleiotropic association with CRP.Methods and resultsWe selected and analyzed 16 CRP-associated and 250 inflammation-related GWAS SNPs among 40 473 African American, American Indian, Asian/Pacific Islander, European American, and Hispanic participants from 7 studies collaborating in the Population Architecture using Genomics and Epidemiology (PAGE) study. Fixed-effect meta-analyses combined study-specific race/ethnicity-stratified linear regression estimates to evaluate the association between each SNP and high-sensitivity CRP. Overall, 18 SNPs in 8 loci were significantly associated with CRP (Bonferroni-corrected P<3.1×10(-3) for replication, P<2.0×10(-4) for pleiotropy): Seven of these were specific to European Americans, while 9 additionally generalized to African Americans (1), Hispanics (5), or both (3); 1 SNP was seen only in African Americans and Hispanics. Two SNPs in the CELSR2/PSRC1/SORT1 locus showed a potentially novel association with CRP: rs599839 (P=2.0×10(-6)) and rs646776 (P=3.1×10(-5)).ConclusionsWe replicated 16 SNP-CRP associations, 10 of which generalized to African Americans and/or Hispanics. We also identified potentially novel pleiotropic associations with CRP for two SNPs previously associated with coronary artery disease and/or low-density lipoprotein-cholesterol. These findings demonstrate the benefit of evaluating genotype-phenotype associations in multiple race/ethnicity groups and looking for pleiotropic relationships among SNPs previously associated
Published: 2014

32. Interleukin-6 receptor pathways in coronary heart disease : a collaborative meta-analysis of 82 studies

Author: Sarwar, Nadeem, Butterworth, Adam S., Freitag, Daniel F., Gregson, John, Willeit, Peter, Gorman, Donal N., Gao, Pei, Saleheen, Danish, Rendon, Augusto, Nelson, Christopher P., Braund, Peter S., Hall, Alistair S., Chasman, Daniel I., Tybjaerg-Hansen, Anne, Chambers, John C., Benjamin, Emelia J., Franks, Paul W., Clarke, Robert, Wilde, Arthur A. M., Trip, Mieke D., Steri, Maristella, Witteman, Jacqueline C. M., Qi, Lu, van der Schoot, C. Ellen, de Faire, Ulf, Erdmann, Jeanette, Stringham, Heather M., Koenig, Wolfgang, Rader, Daniel J., Melzer, David, Reich, David, Psaty, Bruce M., Kleber, Marcus E., Panagiotakos, Demosthenes B., Willeit, Johann, Wennberg, Patrik, Woodward, Mark, Adamovic, Svetlana, Rimm, Eric B., Meade, Tom W., Gillum, Richard F., Shaffer, Jonathan A., Hofman, Albert, Onat, Altan, Sundström, Johan, Wassertheil-Smoller, Sylvia, Mellstrom, Dan, Gallacher, John, Cushman, Mary, Tracy, Russell P., Kauhanen, Jussi, Karlsson, Magnus, Salonen, Jukka T., Wilhelmsen, Lars, Amouyel, Philippe, Cantin, Bernard, Best, Lyle G., Ben-Shlomo, Yoav, Manson, JoAnn E., Davey-Smith, George, de Bakker, Paul I. W., O'Donnell, Christopher J., Wilson, James F., Wilson, Anthony G., Assimes, Themistocles L., Jansson, John-Olov, Ohlsson, Claes, Tivesten, Asa, Ljunggren, Östen, Reilly, Muredach P., Hamsten, Anders, Ingelsson, Erik, Cambien, Francois, Hung, Joseph, Thomas, G. Neil, Boehnke, Michael, Schunkert, Heribert, Asselbergs, Folkert W., Kastelein, John J. P., Gudnason, Vilmundur, Salomaa, Veikko, Harris, Tamara B., Kooner, Jaspal S., Allin, Kristine H., Nordestgaard, Borge G., Hopewell, Jemma C., Goodall, Alison H., Ridker, Paul M., Holm, Hilma, Watkins, Hugh, Ouwehand, Willem H., Samani, Nilesh J., Kaptoge, Stephen, Di Angelantonio, Emanuele, Harari, Olivier, Danesh, John, Sarwar, Nadeem, Butterworth, Adam S., Freitag, Daniel F., Gregson, John, Willeit, Peter, Gorman, Donal N., Gao, Pei, Saleheen, Danish, Rendon, Augusto, Nelson, Christopher P., Braund, Peter S., Hall, Alistair S., Chasman, Daniel I., Tybjaerg-Hansen, Anne, Chambers, John C., Benjamin, Emelia J., Franks, Paul W., Clarke, Robert, Wilde, Arthur A. M., Trip, Mieke D., Steri, Maristella, Witteman, Jacqueline C. M., Qi, Lu, van der Schoot, C. Ellen, de Faire, Ulf, Erdmann, Jeanette, Stringham, Heather M., Koenig, Wolfgang, Rader, Daniel J., Melzer, David, Reich, David, Psaty, Bruce M., Kleber, Marcus E., Panagiotakos, Demosthenes B., Willeit, Johann, Wennberg, Patrik, Woodward, Mark, Adamovic, Svetlana, Rimm, Eric B., Meade, Tom W., Gillum, Richard F., Shaffer, Jonathan A., Hofman, Albert, Onat, Altan, Sundström, Johan, Wassertheil-Smoller, Sylvia, Mellstrom, Dan, Gallacher, John, Cushman, Mary, Tracy, Russell P., Kauhanen, Jussi, Karlsson, Magnus, Salonen, Jukka T., Wilhelmsen, Lars, Amouyel, Philippe, Cantin, Bernard, Best, Lyle G., Ben-Shlomo, Yoav, Manson, JoAnn E., Davey-Smith, George, de Bakker, Paul I. W., O'Donnell, Christopher J., Wilson, James F., Wilson, Anthony G., Assimes, Themistocles L., Jansson, John-Olov, Ohlsson, Claes, Tivesten, Asa, Ljunggren, Östen, Reilly, Muredach P., Hamsten, Anders, Ingelsson, Erik, Cambien, Francois, Hung, Joseph, Thomas, G. Neil, Boehnke, Michael, Schunkert, Heribert, Asselbergs, Folkert W., Kastelein, John J. P., Gudnason, Vilmundur, Salomaa, Veikko, Harris, Tamara B., Kooner, Jaspal S., Allin, Kristine H., Nordestgaard, Borge G., Hopewell, Jemma C., Goodall, Alison H., Ridker, Paul M., Holm, Hilma, Watkins, Hugh, Ouwehand, Willem H., Samani, Nilesh J., Kaptoge, Stephen, Di Angelantonio, Emanuele, Harari, Olivier, and Danesh, John
Abstract: Background: Persistent inflammation has been proposed to contribute to various stages in the pathogenesis of cardiovascular disease. Interleukin-6 receptor (IL6R) signalling propagates downstream inflammation cascades. To assess whether this pathway is causally relevant to coronary heart disease, we studied a functional genetic variant known to affect IL6R signalling. Methods: In a collaborative meta-analysis, we studied Asp358Ala (rs2228145) in IL6R in relation to a panel of conventional risk factors and inflammation biomarkers in 125 222 participants. We also compared the frequency of Asp358Ala in 51 441 patients with coronary heart disease and in 136 226 controls. To gain insight into possible mechanisms, we assessed Asp358Ala in relation to localised gene expression and to postlipopolysaccharide stimulation of interleukin 6. Findings: The minor allele frequency of Asp358Ala was 39%. Asp358Ala was not associated with lipid concentrations, blood pressure, adiposity, dysglycaemia, or smoking (p value for association per minor allele >= 0.04 for each). By contrast, for every copy of 358Ala inherited, mean concentration of IL6R increased by 34.3% (95% CI 30.4-38.2) and of interleukin 6 by 14.6% (10.7-18.4), and mean concentration of C-reactive protein was reduced by 7.5% (5.9-9.1) and of fibrinogen by 1.0% (0.7-1.3). For every copy of 358Ala inherited, risk of coronary heart disease was reduced by 3.4% (1.8-5.0). Asp358Ala was not related to IL6R mRNA levels or interleukin-6 production in monocytes. Interpretation: Large-scale human genetic and biomarker data are consistent with a causal association between IL6R-related pathways and coronary heart disease.
Published: 2012
Full Text: View/download PDF

33. Interleukin-6 receptor pathways in coronary heart disease : a collaborative meta-analysis of 82 studies

Author: Sarwar, Nadeem, Butterworth, Adam S., Freitag, Daniel F., Gregson, John, Willeit, Peter, Gorman, Donal N., Gao, Pei, Saleheen, Danish, Rendon, Augusto, Nelson, Christopher P., Braund, Peter S., Hall, Alistair S., Chasman, Daniel I., Tybjaerg-Hansen, Anne, Chambers, John C., Benjamin, Emelia J., Franks, Paul W., Clarke, Robert, Wilde, Arthur A. M., Trip, Mieke D., Steri, Maristella, Witteman, Jacqueline C. M., Qi, Lu, van der Schoot, C. Ellen, de Faire, Ulf, Erdmann, Jeanette, Stringham, Heather M., Koenig, Wolfgang, Rader, Daniel J., Melzer, David, Reich, David, Psaty, Bruce M., Kleber, Marcus E., Panagiotakos, Demosthenes B., Willeit, Johann, Wennberg, Patrik, Woodward, Mark, Adamovic, Svetlana, Rimm, Eric B., Meade, Tom W., Gillum, Richard F., Shaffer, Jonathan A., Hofman, Albert, Onat, Altan, Sundstrom, Johan, Wassertheil-Smoller, Sylvia, Mellstrom, Dan, Gallacher, John, Cushman, Mary, Tracy, Russell P., Kauhanen, Jussi, Karlsson, Magnus, Salonen, Jukka T., Wilhelmsen, Lars, Amouyel, Philippe, Cantin, Bernard, Best, Lyle G., Ben-Shlomo, Yoav, Manson, JoAnn E., Davey-Smith, George, de Bakker, Paul I. W., O'Donnell, Christopher J., Wilson, James F., Wilson, Anthony G., Assimes, Themistocles L., Jansson, John-Olov, Ohlsson, Claes, Tivesten, Asa, Ljunggren, Osten, Reilly, Muredach P., Hamsten, Anders, Ingelsson, Erik, Cambien, Francois, Hung, Joseph, Thomas, G. Neil, Boehnke, Michael, Schunkert, Heribert, Asselbergs, Folkert W., Kastelein, John J. P., Gudnason, Vilmundur, Salomaa, Veikko, Harris, Tamara B., Kooner, Jaspal S., Allin, Kristine H., Nordestgaard, Borge G., Hopewell, Jemma C., Goodall, Alison H., Ridker, Paul M., Holm, Hilma, Watkins, Hugh, Ouwehand, Willem H., Samani, Nilesh J., Kaptoge, Stephen, Di Angelantonio, Emanuele, Harari, Olivier, Danesh, John, Sarwar, Nadeem, Butterworth, Adam S., Freitag, Daniel F., Gregson, John, Willeit, Peter, Gorman, Donal N., Gao, Pei, Saleheen, Danish, Rendon, Augusto, Nelson, Christopher P., Braund, Peter S., Hall, Alistair S., Chasman, Daniel I., Tybjaerg-Hansen, Anne, Chambers, John C., Benjamin, Emelia J., Franks, Paul W., Clarke, Robert, Wilde, Arthur A. M., Trip, Mieke D., Steri, Maristella, Witteman, Jacqueline C. M., Qi, Lu, van der Schoot, C. Ellen, de Faire, Ulf, Erdmann, Jeanette, Stringham, Heather M., Koenig, Wolfgang, Rader, Daniel J., Melzer, David, Reich, David, Psaty, Bruce M., Kleber, Marcus E., Panagiotakos, Demosthenes B., Willeit, Johann, Wennberg, Patrik, Woodward, Mark, Adamovic, Svetlana, Rimm, Eric B., Meade, Tom W., Gillum, Richard F., Shaffer, Jonathan A., Hofman, Albert, Onat, Altan, Sundstrom, Johan, Wassertheil-Smoller, Sylvia, Mellstrom, Dan, Gallacher, John, Cushman, Mary, Tracy, Russell P., Kauhanen, Jussi, Karlsson, Magnus, Salonen, Jukka T., Wilhelmsen, Lars, Amouyel, Philippe, Cantin, Bernard, Best, Lyle G., Ben-Shlomo, Yoav, Manson, JoAnn E., Davey-Smith, George, de Bakker, Paul I. W., O'Donnell, Christopher J., Wilson, James F., Wilson, Anthony G., Assimes, Themistocles L., Jansson, John-Olov, Ohlsson, Claes, Tivesten, Asa, Ljunggren, Osten, Reilly, Muredach P., Hamsten, Anders, Ingelsson, Erik, Cambien, Francois, Hung, Joseph, Thomas, G. Neil, Boehnke, Michael, Schunkert, Heribert, Asselbergs, Folkert W., Kastelein, John J. P., Gudnason, Vilmundur, Salomaa, Veikko, Harris, Tamara B., Kooner, Jaspal S., Allin, Kristine H., Nordestgaard, Borge G., Hopewell, Jemma C., Goodall, Alison H., Ridker, Paul M., Holm, Hilma, Watkins, Hugh, Ouwehand, Willem H., Samani, Nilesh J., Kaptoge, Stephen, Di Angelantonio, Emanuele, Harari, Olivier, and Danesh, John
Abstract: Background Persistent inflammation has been proposed to contribute to various stages in the pathogenesis of cardiovascular disease. Interleukin-6 receptor (IL6R) signalling propagates downstream inflammation cascades. To assess whether this pathway is causally relevant to coronary heart disease, we studied a functional genetic variant known to affect IL6R signalling. Methods In a collaborative meta-analysis, we studied Asp358Ala (rs2228145) in IL6R in relation to a panel of conventional risk factors and inflammation biomarkers in 125 222 participants. We also compared the frequency of Asp358Ala in 51 441 patients with coronary heart disease and in 136 226 controls. To gain insight into possible mechanisms, we assessed Asp358Ala in relation to localised gene expression and to postlipopolysaccharide stimulation of interleukin 6. Findings The minor allele frequency of Asp358Ala was 39%. Asp358Ala was not associated with lipid concentrations, blood pressure, adiposity, dysglycaemia, or smoking (p value for association per minor allele >= 0.04 for each). By contrast, for every copy of 358Ala inherited, mean concentration of IL6R increased by 34.3% (95% CI 30.4-38.2) and of interleukin 6 by 14.6% (10.7-18.4), and mean concentration of C-reactive protein was reduced by 7.5% (5.9-9.1) and of fibrinogen by 1.0% (0.7-1.3). For every copy of 358Ala inherited, risk of coronary heart disease was reduced by 3.4% (1.8-5.0). Asp358Ala was not related to IL6R mRNA levels or interleukin-6 production in monocytes. Interpretation Large-scale human genetic and biomarker data are consistent with a causal association between IL6R-related pathways and coronary heart disease.
Published: 2012
Full Text: View/download PDF

34. Genetic and other factors determining mannose-binding lectin levels in American Indians: the Strong Heart Study

Author: Best, Lyle G, Ferrell, Robert E, Decroo, Susan, North, Kari E, Maccluer, Jean W, Zhang, Ying, Lee, Elisa T, Howard, Barbara V, Umans, Jason, Palmieri, Vittorio, Garred, Peter, Best, Lyle G, Ferrell, Robert E, Decroo, Susan, North, Kari E, Maccluer, Jean W, Zhang, Ying, Lee, Elisa T, Howard, Barbara V, Umans, Jason, Palmieri, Vittorio, and Garred, Peter
Abstract: Udgivelsesdato: 2009, BACKGROUND: Mannose-binding lectin (MBL) forms an integral part of the innate immune system. Persistent, subclinical infections and chronic inflammatory states are hypothesized to contribute to the pathogenesis of atherosclerosis. MBL gene (MBL2) variants with between 12 to 25% allele frequency in Caucasian and other populations, result in markedly reduced expression of functional protein. Prospective epidemiologic studies, including a nested, case-control study from the present population, have demonstrated the ability of MBL2 genotypes to predict complications of atherosclerosis,. The genetic control of MBL2 expression is complex and genetic background effects in specific populations are largely unknown. METHODS: The Strong Heart Study is a longitudinal, cohort study of cardiovascular disease among American Indians. A subset of individuals genotyped for the above mentioned case-control study were selected for analysis of circulating MBL levels by double sandwich ELISA method. Mean MBL levels were compared between genotypic groups and multivariate regression was used to determine other independent factors influencing MBL2 expression. RESULTS: Our results confirm the effects of variant structural (B, C, and D) and promoter (H and Y) alleles that have been seen in other populations. In addition, MBL levels were found to be positively associated with male gender and hemoglobin A1c levels, but inversely related to triglyceride levels. Correlation was not found between MBL and other markers of inflammation. CONCLUSION: New data is presented concerning the effects of known genetic variants on MBL levels in an American Indian population, as well as the relationship of MBL2 expression to clinical and environmental factors, including inflammatory markers.
Published: 2009

35. Genetic and other factors determining mannose-binding lectin levels in American Indians: the Strong Heart Study

Author: Best, Lyle G, Ferrell, Robert E, Decroo, Susan, North, Kari E, Maccluer, Jean W, Zhang, Ying, Lee, Elisa T, Howard, Barbara V, Umans, Jason, Palmieri, Vittorio, Garred, Peter, Best, Lyle G, Ferrell, Robert E, Decroo, Susan, North, Kari E, Maccluer, Jean W, Zhang, Ying, Lee, Elisa T, Howard, Barbara V, Umans, Jason, Palmieri, Vittorio, and Garred, Peter
Abstract: Udgivelsesdato: 2009, BACKGROUND: Mannose-binding lectin (MBL) forms an integral part of the innate immune system. Persistent, subclinical infections and chronic inflammatory states are hypothesized to contribute to the pathogenesis of atherosclerosis. MBL gene (MBL2) variants with between 12 to 25% allele frequency in Caucasian and other populations, result in markedly reduced expression of functional protein. Prospective epidemiologic studies, including a nested, case-control study from the present population, have demonstrated the ability of MBL2 genotypes to predict complications of atherosclerosis,. The genetic control of MBL2 expression is complex and genetic background effects in specific populations are largely unknown. METHODS: The Strong Heart Study is a longitudinal, cohort study of cardiovascular disease among American Indians. A subset of individuals genotyped for the above mentioned case-control study were selected for analysis of circulating MBL levels by double sandwich ELISA method. Mean MBL levels were compared between genotypic groups and multivariate regression was used to determine other independent factors influencing MBL2 expression. RESULTS: Our results confirm the effects of variant structural (B, C, and D) and promoter (H and Y) alleles that have been seen in other populations. In addition, MBL levels were found to be positively associated with male gender and hemoglobin A1c levels, but inversely related to triglyceride levels. Correlation was not found between MBL and other markers of inflammation. CONCLUSION: New data is presented concerning the effects of known genetic variants on MBL levels in an American Indian population, as well as the relationship of MBL2 expression to clinical and environmental factors, including inflammatory markers.
Published: 2009

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35 results on '"Best, Lyle G"'

1. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas

2. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas

3. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

4. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

5. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

6. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

7. Lipidomics profiling of biological aging in American Indians: the Strong Heart Family Study.

8. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

9. Diversifying the genomic data science research community

10. Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease.

11. Diversifying the Genomic Data Science Research Community

12. Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study.

13. Discovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study.

14. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

15. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

16. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

17. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

18. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

19. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

20. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis.

21. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

22. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

23. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction:An Individual-Participant-Data Meta-Analysis

24. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

25. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

26. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction:An Individual-Participant-Data Meta-Analysis

27. Arsenic Exposure, Arsenic Metabolism, and Incident Diabetes in the Strong Heart Study

28. Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

29. Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

30. Multiancestral analysis of inflammation-related genetic variants and C-reactive protein in the population architecture using genomics and epidemiology study.

31. Multiancestral analysis of inflammation-related genetic variants and C-reactive protein in the population architecture using genomics and epidemiology study.

32. Interleukin-6 receptor pathways in coronary heart disease : a collaborative meta-analysis of 82 studies

33. Interleukin-6 receptor pathways in coronary heart disease : a collaborative meta-analysis of 82 studies

34. Genetic and other factors determining mannose-binding lectin levels in American Indians: the Strong Heart Study

35. Genetic and other factors determining mannose-binding lectin levels in American Indians: the Strong Heart Study

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35 results on '"Best, Lyle G"'

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